Science + AI

Tweets about the Science + AI

[Yujin Potter @ ICML 🇰🇷 @yujink_]

1/N

Neuroscience and social science research on humans has shown:
– Similar brain activity predicts friendship and cooperation
– Diverse minds drive innovation

We wondered whether AI-AI interaction would show the same pattern.

It does. LLMs with similar internal representations cooperate more, but produce less novel output.

🧵 (ICML 2026)

[Miles Cranmer @MilesCranmer]

This is an insane paper and I love it

https://t.co/DP8OR5NJf2

[Mario Krenn @MarioKrenn6240]

Wonderful issue of Daedalus is out on
𝗔𝗜 & 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: 𝗪𝗵𝗮𝘁 𝗜𝘀 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆?
edited by James Manyika

Exceptional contributions by @demishassabis, @ylecun, Alán Aspuru-Guzik, @pushmeet, Joshua Tenenbaum, @Carla Gomes, @cosmo_shirley, @AnimaAnandkumar, Hartmut Neven, @EricTopol and many others.

We (together with Heather Champion) contributed a text of what might be necessary for the future of science in the age of artificial scientists:
𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆 𝗼𝗳 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: 𝗧𝗲𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗖𝗼𝗺𝗶𝗻𝗴 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀

Thanks so much James Manyika for the invitation and for creating such a beautiful issue! 🤩

[Brandon Stewart @b_m_stewart]

1/ New @Nature! We study how powerful institutions shape the information environment for LLMs. Commercial LLM training is opaque, so we trace a path from state-coordinated media -> training data -> model responses.

[Yian Yin @yian_yin]

📄 Excited to share our latest preprint: the first cross-field audit of LLM-hallucinated citations in science
⚠️ Across arXiv, bioRxiv, SSRN & PMC, we estimate 147K fake citations in 2025 alone — threatening both the quality and equity of scientific work.

[Retraction Watch @RetractionWatch]

"AI agents may be skilled researchers—but not always honest ones."
https://t.co/8aMLT6ZZKw
And "The AI scientist: now academic papers can be fully automated, what does this mean for the future of research?"
https://t.co/ZHY0ujx4xw

[Shahan @IC2S2 @shahanmemon]

Our position paper, "AI Welfare Is Bullshit" just got accepted to ICML 2026 @icmlconf!

Preprint link 👇

[Eamon Duede @phil_of_ai]

Excited this piece is out in #Philosophy of #Science. We argue that, paradoxically, we can have certainty about some theorems, the proofs of which we can never understand. That's weird & tells us something important about why we do #math in the first place https://t.co/uKD5zz8C9P

[Reviewer3 @Reviewer3]

No, AI should not be used to make decisions about manuscripts or "score" novelty or significance.

That's not what it's good at, anyways.

Data from our benchmark of over 145,000+ comments shows AI focuses primarily on technical verification - like the validity, sufficiency, and transparency of the work.

Evaluating contribution is uniquely human.

[Jeremy Nguyen ✍🏼 🚢 @JeremyNguyenPhD]

Claude Code for Academics

"A gentle introduction in how to use Claude Code for Academics."
presentation slides and github repo from Alessandro Spina

link in reply

[Divy Thakkar @divy93t]

Anthropic drops “largest qualitative study ever ” and it’s very well produced with moving quotes. Does that mean we truly understand what users want of their AI? Is this a large-scale survey where participants answered four structured questions – yes! Is it robust qual research? I have concerns about the method, and why the generality of claims is a stretch. 🧵

[Shahan @IC2S2 @shahanmemon]

This is conceptually so weird because imho qualitative research is not evaluated by scale but by depth, context, and human interpretation.
Stop assuming that “hand-wavey-ness” is a qual problem just because qual work doesn’t look like large-N quantitative analysis.

[Ai2 @allen_ai]

Can AI predict what scientists will do next—not just one piece, but the whole research process? PreScience is our new model eval for forecasting how science unfolds end-to-end, from how research teams form to a paper's eventual impact. Built with @UChicago, supported by @NSF.

[Er-Te Zheng @ErteZheng]

I just launched the RetractionRisk Scanner browser extension! 🚀 With just one click, instantly check if a paper has flagged issues or retraction risks while reading. Available now on Chrome & Firefox. Try it out! 👇https://t.co/Tzwas0eaIQ

[Sam Rodriques @SGRodriques]

The next round of FutureHouse Postdoctoral Fellowships is due next week! Apply our AI tools to specific problems in biology and biochemistry, in collaboration with world-leading academic labs:

--$125,000 annual stipend.
--Access to all tools developed by FutureHouse and Edison Scientific at scale, including Kosmos and several as-of-yet unreleased agents, with under-the-hood access to them to specialize them for your workflows.
--Receive dedicated software engineering support.
--1 year with possible 1 year extension.

Even more exceptional co-advisors than last year. Deadline for applications is February 13th, 2026. Link in next post.

[Akari Asai @AkariAsai]

Thrilled to share: OpenScholar - our work on scientific deep research agents for reliable literature synthesis -has been accepted to Nature! 🎉 Huge thanks to collaborators across institutions who made this possible!

[VraserX e/acc @VraserX]

Demis Hassabis, CEO of Google DeepMind, drops a quiet bombshell:

The big question isn’t whether AI can solve problems.
It’s whether AI can invent new science.

Right now, it can’t.

Not because of compute. Not because of data.
But because it lacks something fundamental:

A world model.

Today’s LLMs can generate brilliant text, images, even code.
But they don’t truly understand causality.
They don’t know why A leads to B. They just predict patterns.

Hassabis argues that real scientific discovery requires more:
– Long-term planning
– Stronger reasoning
– And an internal model of how the world works

Physics. Biology. Cause and effect.

Only then can an AI run its own thought experiments.
Only then do we get a true digital scientist.

[Anthropic @AnthropicAI]

Since launching our AI for Science program, we’ve been working with scientists to understand how AI is accelerating progress.

We spoke with 3 labs where Claude is reshaping research—and starting to point towards novel scientific insights and discoveries.
https://t.co/WAvghBlbsC

[James Evans @profjamesevans]

Thrilled to share our Nature paper, out today, on how AI use has shaped scientific careers and science as a whole (in collaboration with the amazing Qianyue Hao, @xu_fengli, and Li Yong from Tsinghua University and Zhongguancun Academy.)

We analyzed tens of millions of research papers spanning four decades of natural science to understand how AI is reshaping science. The findings reveal a paradox.

For individual scientists, AI is a career accelerator. Researchers who adopt AI publish 3 times more papers with fewer authors, receive 5 times more citations, and become research leaders more than a year earlier than peers who don't. AI papers appear ~20% more frequently in top-quartile journals. Annual citations run 100% higher than non-AI papers across three decades of follow-up.

For science as a whole, AI is a narrowing force. AI-augmented research covers ~5% less topical ground and generates a quarter less engagement among follow-on researchers. The contraction appears in the vast majority of the 200+ subfields we examined. Citation patterns show a starker concentration: in AI research, just 22% of papers capture 80% of all citations.

The mechanism is straightforward: AI use has shifted to where data is abundant, at an accelerating pace as models have grown larger. AI gravitates toward well-lit problems and away from foundational and emergent questions where data is necessarily sparse. The result is collective hill-climbing—everyone scaling the same popular peaks rather than searching for higher mountains.

This creates "lonely crowds" in the scientific literature: clusters of researchers converging on identical problems without building on each other's work.

The pattern holds across biology, chemistry, physics, medicine, materials science, and geology. It persists and increases through each wave of AI—from conventional machine learning through deep learning to today's generative models and LLMs.

This isn't inevitable. Models that are powerful at prediction can be inverted to identify what is surprising (to those predictions), and enable us to consider and theorize entailments to surprising new data and findings. But without deliberate intervention, local incentives have and will likely continue to push scientists to optimize and compress what's already known rather than discover what isn't.

The history of major discoveries is linked to new ways of seeing nature. If we want AI to accelerate breakthroughs rather than automate the familiar, we need AI systems tuned to surprise that expand sensory and experimental capacity—not just cognition.

Paper: https://t.co/SbFaEif67m

Science commentary: https://t.co/iQdau0cIL5
Nature commentary: https://t.co/qFWoTHXmdR
Nature podcast: https://t.co/4xkWwH3EgT

[Yian Yin @yian_yin]

Our latest in @ScienceMagazine: Scientific production in the ear of LLMs: https://t.co/C5xMiV8KKQ